Anticipating the Unanticipated-Unintended Consequences of Scientific and Technological Purposive Actions

Tonn, Bruce; Stiefel, Dorian

doi:10.1177/1946756718789413

Cited by 9 publications

(8 citation statements)

References 73 publications

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“…Humans are very good at declaring rules for a given domain but very poor at clearly understanding the potential emergent behaviors which might arise from exhaustively applying those rules in different circumstances. 13 A strength of the Monterey Phoenix environment is that it exhaustively generates all possible event traces of applying a given set of rules up to a given scope of repeatedly executing the set of rules. Monterey Phoenix can generate the various use cases of interest for system engineers in developing system architectures for complex systems and can generate the finite state machines needed by engineers to implement the interfaces necessary to integrate logical system components with physical system components.…”

Section: Findings and Recommendationsmentioning

confidence: 99%

State machine execution traces for verifying and validating robot behaviors

Errico,

Giammarco,

Dyer

et al. 2023

Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023

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This work demonstrates that a novice user of the Monterey Phoenix (MP) tool can encode their expertise of robot mission tasks in the form of a finite state machine. State machine diagrams are among several approaches available for describing behavior. They are appropriate for capturing and understanding behavior rules of systems that can be described in terms of possible states and state transitions. Tools that offer state machine automation enable modelers to test the activation of states and trace the transitions between states, providing assistance with verification and validation of the behavior logic. The United States Military Academy (USMA) at West Point's Robotics Research Center (RRC) in collaboration with the Naval Postgraduate School (NPS), developed a minimal executable state machine model to test its implementation of behavior logic for a single robot agent in Project Aquaticus, a human-robot teaming capture the flag competition. Monterey Phoenix, a formal behavior modeling language, approach, and tool was used to express the behavior logic of the robots participating in this game as a finite state machine model. MP was used to generate every possible trace through the state machine behavior logic up to the specified scope limit. This model informed the team's understanding of the desired states for the single robot competitor in Aquaticus, assisted in identifying behavior improvements, and identifying dependencies among behaviors and where the process could possibly go wrong. In essence, this work demonstrates that encoding the finite state machine of the single robot tasks in MP allows experts to verify and validate expected robot behaviors and adjust as needed. Future work will look to expand from the single agent modeling to multi-agent modeling in MP to generate and verify possible sequences of events for the Aquaticus competition between competing teams. By using this modeling method to understand the state of each actor and their response to inputs from other actors, the team expects to improve the communication within the human-robot team and ultimately achieve a better understanding of command intent in complex, contested environments. Today's mission-critical systems are actually, "systems of systems" with complexity that will surpass the designer's cognitive ability to anticipate all interactions. Without tools, languages, and methodologies to analyze system-level behavior early in the design, mission systems can be at risk of emergent behaviors that may negatively impact safety and security. Our team believes that the MP environment can assist commanders in anticipating potentially unsafe or insecure emergent behaviors of human-robot teams in complex, contested environments and adjust human intent/command intent accordingly for allowable combat crew drill behaviors required to achieve assigned tasks and missions.

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Section: Findings and Recommendationsmentioning

confidence: 99%

State machine execution traces for verifying and validating robot behaviors

Errico,

Giammarco,

Dyer

et al. 2023

Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023

View full text Add to dashboard Cite

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“…The term unintended consequences refers to unintentional outcomes whether they are anticipated ("foreseen") or unanticipated ("unforeseen"). [9][10][11][12][13] Previous research on telehealth has focused by and large on the expected benefits [20][21][22] as well as some of its challenges. 8,23,24 However, its unintended consequences/outcomes and the factors influencing these outcomes have been little documented.…”

Section: Highlightsmentioning

confidence: 99%

“…Although often used interchangeably, unintended consequences and unanticipated consequences are not the same. The term unintended consequences refers to unintentional outcomes whether they are anticipated (“foreseen”) or unanticipated (“unforeseen”) 9–13 …”

Section: Introductionmentioning

confidence: 99%

Systems archetypes to investigate the unintended consequences of telehealth in rural Australia: A systems thinking approach to telehealth evaluation and policymaking

Osman,

Churruca,

Ellis

et al. 2023

Health Planning & Management

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As an alternative model of delivery to standard care, telehealth offers a promising solution to health access issues faced by rural and remote communities in Australia and worldwide. However, research typically focuses on its expected benefits and pitfalls, with little to no consideration of its unintended consequences and factors influencing its better utilisation. Drawing on systems thinking and informed by complexity science, we propose using systems archetypes—systems thinking tools ‐ as a magnifying lens to investigate potential telehealth unintended consequences or outcomes. We conceptualise telehealth implementation in rural and remote Australia as a sociotechnical system whereby the interactions between its various agents shape telehealth implementation and, in turn, are shaped by it. When introducing new policies or interventions to any system, these interactions often lead to outcomes other than those initially planned or intended. Although systems archetypes cannot necessarily predict these outcomes, they are valuable for helping anticipate unintended, unforeseen outcomes and facilitating discussions about them to mitigate their negative impact and maximise their benefits. Outcomes are not necessarily adverse; they can also be positive. So, investigating such outcomes will minimise their negative impact and maximise their benefit. Our method was to review existing research and a selection of complexity and systems informed frameworks. Then, we assessed systems archetypes. And how they can be utilised to investigate unintended consequences. A worked example of what an unintended consequence in the implementation of telehealth in rural and remote Australia is presented.

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“…The introduction of technology in close interaction with the human function may have outcomes that are not intended or foreseen. These outcomes may mean additional work for practitioners, changes in communication patterns and workflows, often fostering an overdependence on technology [42]. These unintended consequences can be both benefits and drawbacks, sometimes with results, contrary to what was initially intended (Table 2).…”

Section: Unintended Consequences Of Benefits -The Learnings From Inno...mentioning

confidence: 99%

Assistive Healthcare Robotics – Challenges in Nursing Service Innovation: Critical Review

Badr

Dankar

2022

ITM Web Conf.

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Healthcare robots assist older people and caregivers. Many scientists and researchers have been looking at the use of robotic technology to help not only elderly people but also their care providers. Through a review of the literature and an in-depth study of published papers, we include a timely overview of care robotics. In this study, we review what we know about the use of assistive robots in the elderly care context, their benefits and potential challenges. These humanoid resources offer a range of physical, cognitive, and social tasks in the aim of improving health outcomes. Service Science calls for understanding complex service systems, our Sociotechnical system (STS) lens applied to the use of robotics in the nursing function aims at learning the underlying trajectories of socio-technical dynamics within the nursing service system and the complexity introduced by their humanoid counterpart. This presents a significant contribution to Service Science. In essence, the research introduces the STS lens to understand the interaction between human and machine in the nursing ecosystem, while proposing learnings on a better design in practice. We focus on the current and potential future challenges of healthcare robotics as well as how such technology can help healthy aging, healthcare staff, especially nurses, and our healthcare system as a whole. We conclude that, despite the potential advantages, the adoption of care robots is still shy. Using the lens of the sociotechnical perspective and concepts of service innovation roadmaps, we pave the way for identifying factors of adoption that may influence the proliferation of care robots.

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Anticipating the Unanticipated-Unintended Consequences of Scientific and Technological Purposive Actions

Cited by 9 publications

References 73 publications

State machine execution traces for verifying and validating robot behaviors

State machine execution traces for verifying and validating robot behaviors

Systems archetypes to investigate the unintended consequences of telehealth in rural Australia: A systems thinking approach to telehealth evaluation and policymaking

Assistive Healthcare Robotics – Challenges in Nursing Service Innovation: Critical Review

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